Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding

arXiv:2403.15438v1 Announce Type: cross
Abstract: In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. Interestingly, our method does not require retraining the model, as it consists in using a frozen efficient deep learning backbone while continuously realigning data, both at input and latent spaces, based on streaming observations. We demonstrate its efficiency for Motor Imagery brain decoding from electroencephalography data, considering challenging cross-subject scenarios. For reproducibility, …

abstract arxiv bci brain computer context cs.lg decoding deep learning eess.sp interfaces offline performance retraining supervision type unsupervised